A fingerprint is characterised by smoothly flowing ridges and valleys, characterised by their orientation, separation, shape and minutiae. Minutiae are ridge endings and ridge bifurcations.
Traditionally, fingerprints have been the most widely accepted biometric. The formation and distinctiveness of the fingerprint has been understood since the early twentieth century (see for example Handbook of Fingerprint Recognition, D. Maltoni, et al, Springer 2003).
The science of fingerprints is based on three fundamental principles:                1. Individual epidermal ridges and furrows have different characteristics for different fingerprints.        2. The configuration types are individually variable, but they vary within limits that allows for a systematic classification.        3. The configurations and minutiae details of individual ridges and furrows are permanent and unchanging.With this in mind, many techniques have been developed so that personal identification can be made using fingerprints and an individual can be identified by searching a database that contain a large number of fingerprint images (templates) that represent those fingerprint images.        
Most automatic fingerprint matching algorithms use minutiae information to determine whether two fingerprints are from the same finger. Some techniques use other ridge features (e.g. ridge direction, ridge spacing, ridge shape etc).
Broadly speaking, the process of fingerprint verification/identification involves two phases: (1) enrolment and; and (2) matching.
In the enrolment phase, people's fingerprint image(s) are processed by computer programs and converted into a template. The template is then associated with meta-data of a person's identity (e.g. name, age, sex, address, etc) and stored in a database. During enrolment, acquired fingerprints are stored in a template database, where only those features of the print which are distinguishing, are extracted and represented in some form.
In the matching phase, in verification mode (1:1 matching), a person's fingerprint images will be matched against the template, which belong to the claimed identity, whereas during identification mode (1:N matching), a person's fingerprint images will be matched against all or a subset of templates stored in the database. A matching score may be calculated for each comparison of the test fingerprint to a stored template.
Thus, a newly presented test fingerprint is compared against the set of stored templates and a matching score is returned. Because the test print has to be compared with each stored template, it is necessary to convert it also into the same representation as the template. Then the system can return a score based on how close is the presented (test) print with each template. If this score value is sufficiently high, determined by a user-defined threshold, then a match is declared.
When analyzed at different levels, a fingerprint pattern exhibits different types of features:                1. At a first level, the ridge-flow forms a particular pattern configuration which can be broadly classified as left loop, right loop, whorl, arch and tented arch. These distinctions are insufficient to facilitate accurate fingerprint matching, but are nevertheless useful in categorising and indexing fingerprint images.        2. At a second level, the geometric location of each minutiae on the print is extracted and the relationship of second level details enables individualisation.        3. At a third level, intra-ridge details can be detected, which are essentially sweat pores whose position, shape and distribution are considered highly distinctive.        
The third level of analysis is often used manually by fingerprint experts in forensic science when only a partial print can be reliably obtained and the second level data (minutiae) are insufficient to make a conclusive match.
Fingerprint recognition systems can be broadly classified as being minutiae based or correlation based.
Minutiae-based approaches first find minutiae points and then map their relative placement on the finger. A global transformation including rotation, shift and scaling can also be identified during the establishment of correspondence between minutiae pairs.
The correlation-based method uses the global pattern of ridges and furrows and calculates a score based on the correlation result. A transformation matrix can be also identified when the correlation result is at a maximum.
FIG. 2 shows a flow chart showing the steps generally performed by a typical prior art system.
At step one, depending on the application, a fingerprint image is acquired through either scanning an inked print or a live finger. Once the image is acquired into the computer memory or on a hard disk, it is often put through an enhancement process to improve the quality of the ridge pattern. This normally includes contrast enhancement, noise removal, filtering and smoothing. Some of the prior art systems also extract the foreground, i.e. ridge pattern from the background, at this step. At step three, either an image correlation method or a feature extraction process will be employed.
FIG. 3 is a flow chart showing a commonly adopted feature extraction technique proposed in “Adaptive flow orientation based feature extraction in fingerprint images”, Journal of Pattern recognition, Vol. 28, no 11, pp 1657-1672 November 1995 and in U.S. Pat. No. 6,049,621.
Firstly, the image is divided into set of blocks and the principal ridge direction of each block is then estimated. A foreground/background segmentation technique is then used to separate the finger part of the image from the background part of the image. At the next step, some binarisation technique is often used to extract the ridge features (labelled as 1) from non-ridge features (labelled as 0). The ridge feature is often more than 1 pixel wide and may contain noisy artefacts. Those artefacts will be removed at the smoothing step and the longer structures are smoothed. At the next step, the smoothed ridge structured is thinned to 1 pixel wide. The location and orientation of the minutiae features are then extracted from the thinned ridge structures. In some systems, a cleanup post-processing step is employed to remove spurious minutiae features.
The fourth step of the matching flow is normally an alignment step. Most of the prior art systems use the minutiae locations or cross-correlation information to identify a global affine transformation to eliminate the geometric variation including shift, and rotation between the query fingerprint and the template fingerprint.
Some prior art systems, e.g. U.S. Pat. No. 6,049,621, describes a method that is able to establish the correspondence between set points in two respective images. The process begins with identifying at least one point in each of the query and template images that correspond, and using this as a reference point. With the information of the location of the reference points and a curved line (thinned ridges) where the points are located, the translation and rotation parameters between the corresponding ridges are then calculated. Further, using the reference points, an index of all possible minutiae pairs between the query and template fingerprint is formed. An iterative process is then employed to identify the transformation (shift and rotation) for each minutiae pair. An alignment score is also calculated for each possible pair, and the pair with the score higher than a pre-defined value is declared as corresponding pairs (pair mate). The advantage of above approach is it can deal with not only a global transformation (shift, rotation and scaling), but also handle the local elastic deformation by calculating the transformation parameter locally. However, the success of this approach is heavily dependent on the quality of feature extraction and ridge detection.
International Publication WO2005/022446 suggested that the location, shape and distribution of sweat pores are highly distinctive and can be used for identification or verification purpose on their own or as supplementary information from minutiae features.
The matching of a password or pin number to another password or pin number involves the comparison of two absolutely defined parameters, facilitating potentially exact matches. The matching of fingerprints or any biometric system, on the other hand, involves the comparison of highly complex functions and is inherently more difficult.
Measurements and matching within biometric systems are subject to two types of errors: a False Match or a False Non Match. The imperfect accuracy of performance is mainly due to the large variability in different impressions of the same finger (intra-class variation), caused by displacement, rotation, partial overlap, elastic deformation, variable pressure, varying skin condition, lighting effects, noise and feature extraction errors. Reliably matching fingerprint images becomes a hard problem when fingerprints from the same finger may look very different, or when fingerprints from different fingers may appear quite similar.
The inconsistency of the extracted minutiae points and ridge structures between the query fingerprint and template fingerprint is caused by several phenomena including:                1. A different area of the fingerprint being scanned between the query and template fingerprint.        2. Irreproducible contact of finger with scanner caused by the result of manual labour, accidents which changes the ridge structure temporarily.        3. Various skin and environmental factors including dry skin, sweat, dirt, humidity, residues left on the glass of scanning devices contribute to the production of low contrast and/or noisy images.        4. The errors/artefacts exists in the feature extraction, ridge detection and thinning process.        
The errors and inconsistency of feature extraction and ridge detection is then propagated to the alignment process, which in turn affects the establishment of a correct correspondence.
In the case of minutiae-based methods, for example, it is difficult to consistently extract the minutiae points when the fingerprint is of low quality or the overlapping area between the data and the template is relatively small. The inconsistency of minutiae extraction will impact on both correspondence and identification of the transformation matrix itself.
Correlation-based techniques are particularly prone to noise, scratches, smudges and are computationally expensive.
Thus, the result of fingerprint matching processes is usually imperfect because of noise in acquisition, variation in the contrast, position and orientation of the print and the elastic deformation of the impression of the finger due to varying degrees of pressure that can be imparted by the subject.
Therefore, prior art fingerprint matching systems can frequently comprise a number of steps to accomplish the match: (1) the acquired images are pre-processed to increase the contrast of the ridge structures; (2) features which are discriminating are extracted from the enhanced ridges, i.e. the minutiae; and (3) the minutiae are compared like for like, after an alignment procedure, with the minutiae of the template. Indeed, in prior-art systems, the template representation may simply be the locations, types (bifurcation or ending), and directions of the minutiae. All three steps have to be repeated for each match before a score can be calculated.
As mentioned above fingerprint systems typically include an alignment process, which occurs prior to any matching, ensuring that images are aligned or justified in order to facilitate an accurate comparison of any two images. This alignment process is usually a combination of translations and rotations, which together form transformation parameters which defines the overall alignment. Prior art alignment methods that depend on minutiae features will inevitably fall into a combinatorial problem of two unknowns, i.e. the correspondence between minutiae points and the transformation between the minutiae set. The transformation parameter calculation depends on the correspondence and the establishment of correspondence rely on an accurate estimation of the transformation parameters. Any errors in either estimate will propagate and degrade the accuracy of the subsequent matching.
Furthermore, because the transformation parameter calculation from the prior art systems rely on the comparison between query and template fingerprints, the process has to be carried out in a pair wise fashion. i.e. the alignment process needs to repeated for each comparison between the query prints and all the templates. In identification applications, when the system is trying to find out who the query fingerprints belongs to from a large database, the alignment procedure has to be repeated multiple times until the identity of the query prints is established.
Another draw back of prior art systems is they only use partial information present in fingerprint images, mainly the ridge pattern and minutiae information. Due to the difficulty of consistent detection and establishing correspondence of sweat pores, those permanent, immutable and individual characteristics are ignored by prior art automatic fingerprint recognition systems. Third level features (sweat pores) are manually identified by fingerprint experts in forensic science because it is sometimes only possible to acquire partial prints, and there may be insufficient second level features present. For certainty skin types or manual labourers, the number of minutiae that can be reliably detected is small and often result a failure at the enrolment stage.
Incorporation of sweat pores can thus enrich the features space by a multiple, and when complimented with minutiae, can reduce the fail-to-enroll rate and increase the matching accuracy. However, although third level analyses are thus considerably more accurate, they also require high resolution scanning. In view of the high density of sweat pores, a consistent feature extraction and correspondence between prints can be difficult to achieve. The high scanning requirements (e.g. 1000 dpi above) has prevented widespread application of third level analyses. However, due to the reducing cost and improving technology of high resolution fingerprint imaging devices, such as WO 2005/022446, there is renewed interest in combining sweat pore representations in order to supplement information from the minutiae features and improve accuracy and usability of fingerprints in non forensic applications.
Therefore, in the light of the above, there is a need for a method which overcomes the deficiencies of prior art systems, by eliminating or reducing intra-class variation in a normalized framework, and has the ability to compliment sweat pore information from the minutiae, which then enables a more reliable and accurate fingerprint match.